Job description

Code smells can be good indicators of poorly designed programs. While lots of resources are available on code smells for text-based programming, most embedded software developed by DAF is developed using Model Driven Design (MDD). In a previous assignment, a student researched code smells and translated them to MDD and even created some specific Model Smells from scratch.
They also made a basic tool that can check for model smells in our models, but it only addresses a limited range of simple model smells. This part II aims to enhance the existing tool.
This task includes:

• Investigate existing tools and methodologies for identifying model smells/quality in software
• Determine criteria for assessing the presence of model smells
• Research and develop algorithms and best practices for detecting complex model smells
• Use a combination of Python and Matlab script to enhance the existing tool, implementing new algorithms and detection methods
• Test the tool extensively, using actual models, to ensure identification of model smells and validating the algorithms
• Document the development process, including methodologies used, design decisions made, and the overall functionality of the enhanced tool

• Extend the tool so it can also dynamically check for issues like signal saturation, a branch that is never reached, etc.
• Explore use cases for modifying models based on some analysis results generated by the tool